Dr. Shovan Bhaumik
Associate Prof., Electrical Engineering Department, Indian Institute of Technology Patna
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Academic Career Length
22Years (2005-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications1 September 2013 in IET Signal Processing
Journal Article
Shovan BhaumikSWSwati
Swati
In this correspondence, the authors develop a novel method based on spherical radial cubature and Gauss–Laguerre quadrature rule for non‐linear state estimation problems. The proposed filter, referred as cubature quadrature Kalman filter (CQKF) would be able to overcome inherent disadvantages associated with the earlier reported cubature Kalman filter (CKF). The theory and formulation of CQKF has been presented. Using two well‐known non‐linear examples, the superior performance of CQKF has been demonstrated. Owing to computational efficiency (compared to the particle and grid‐based filter) and enhanced accuracy compared to the extended Kalman filter and the CKF, the developed algorithm may find place in on‐board real life applications.
1 March 2015 in Journal of the Franklin Institute
Journal Article
MGMahendra Kumar Gupta
Mahendra Kumar GuptaNTNutan Kumar Tomar
Nutan Kumar TomarShovan Bhaumik18 February 2016 in IEEE Transactions on Automatic Control
Journal Article
The traditional Bayesian approximation framework for filtering in discrete time systems assumes that the measurement is available at every time instant. But in practice, the measurements could be randomly delayed. In the literature, the problem has been examined and solution is provided by restricting the maximum number of delay to one or two time steps. This technical note develops an approach to deal with the filtering problems with an arbitrary number of delays in measurement. Pursuing this objective, traditional Bayesian approximation to nonlinear filtering problem is modified by reformulating the expressions of mean and covariances which appear during the measurement update. We use the cubature quadrature rule to evaluate the multivariate integral expressions for the mean vector and the covariance matrix which appear in the developed filtering algorithm. We compare the new algorithm which accounts for delay with the existing CQKF heuristics on two different examples and demonstrate how accounting for a random delay improves the filtering performance.
4 April 2018 in IEEE Journal of Oceanic Engineering
Journal Article
RRRahul Radhakrishnan
Rahul RadhakrishnanShovan BhaumikNTNutan Kumar Tomar
Nutan Kumar TomarIn this paper, a new Gaussian sum (GS) filter with shifted Rayleigh filter (SRF) as proposal has been developed for the bearings-only tracking (BOT) problem. The proposed filter estimates the prior and posterior probability density functions as weighted sum of several Gaussian densities, where the individual Gaussian densities are generated from the SRF. Performance of the proposed filter on the BOT problem is compared with the SRF, the GS sparse-grid Gauss–Hermite filter, and other available GS and quadrature-based filters. Performance of filters is compared in terms of a root-mean-square error (RMSE), track divergence, and computational time. The effect of initial uncertainty, measurement noise covariance, and sampling time on filtering accuracy are also studied. Finally, RMSEs of all the filters are evaluated in comparison with the Cramer–Rao lower bound. From simulation results, it is observed that the performance of the proposed GS-SRF is superior to all other nonlinear filters considered.
1 June 2018 in IEEE Control Systems Letters
Journal Article
RRRahul Radhakrishnan
Rahul RadhakrishnanAYAjay Yadav
Ajay Yadav
PDParesh Date
Paresh DateShovan BhaumikIn this paper, a new method termed as new sigma point Kalman filter (NSKF), is proposed for generating sigma points and weights for estimating the states of a stochastic nonlinear dynamic system. The sigma points and their corresponding weights are generated such that the points nearer to the mean (in inner product sense) have a higher probability of occurrence, and the mean vector and covariance matrix are matched exactly. Performance of the new algorithm is compared with the existing unscented Kalman filter (UKF), the cubature Kalman filter (CKF), the cubature quadrature Kalman filter (CQKF) and higher order unscented filter (HOUF) for two different problems. Comparison is done by calculating the root mean square error (RMSE), relative computational time and track-loss. From simulation results, it can be concluded that the proposed algorithm performs with superior estimation accuracy when compared to the UKF, CKF, CQKF and HOUF.
14 July 2015 in International Journal of Control, Automation, and Systems
Journal Article
ASAbhinoy Kumar Singh
Abhinoy Kumar SinghShovan BhaumikAbout Me
Dr Shovan Bhaumik was born in Kolkata, India, in 1978. He received the B.Sc. degree in Physics in 1999 from Calcutta University, Kolkata, India, the B.Tech degree in Instrumentation and Electronics Engineering in 2002, the Master of Control System Engineering degree in 2004, and the PhD degree in Electrical Engineering in 2009, all from Jadavpur University , Kolkata, India. He is currently Associate Professor of Electrical Engineering Department at Indian Institute of Technology Patna, India. From May 2007 to June 2009, he was a Research Engineer, at GE Global Research, John F Welch Technology Centre, Bangalore, India. From July 2009 to March 2017, he was an Assistant Professor of Electrical Engineering Department at Indian Institute of Technology Patna. Shovan Bhaumik's research interests include nonlinear estimation, statistical signal processing, aerospace and underwater target tracking, networked control systems. He has published more than 20 papers in referred international journals. He is a holder of Young Faculty Research Fellowship (YFRF) award from ministry of Electronics and Information Technology, MeitY, Government of India.